def test_cfm_no_cutoff_time_index(entityset):
es = entityset
agg_feat = Count(es['log']['id'], es['sessions'])
agg_feat4 = Sum(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat4, es['sessions'])
feature_matrix = calculate_feature_matrix([dfeat, agg_feat],
entityset,
instance_ids=[0, 2],
cutoff_time_in_index=False,
approximate=Timedelta(12, 's'),
cutoff_time=[datetime(2013, 4, 9, 10, 31, 19),
datetime(2013, 4, 9, 11, 0, 0)])
assert feature_matrix.index.name == 'id'
assert feature_matrix.index.values.tolist() == [0, 2]
assert feature_matrix[dfeat.get_name()].tolist() == [10, 10]
assert feature_matrix[agg_feat.get_name()].tolist() == [5, 1]
feature_matrix_2 = calculate_feature_matrix([dfeat, agg_feat],
entityset,
instance_ids=[0, 2],
cutoff_time_in_index=False,
approximate=Timedelta(10, 's'),
cutoff_time=[datetime(2011, 4, 9, 10, 31, 19),
datetime(2011, 4, 9, 11, 0, 0)])
assert feature_matrix_2.index.name == 'id'
assert feature_matrix_2.index.tolist() == [0, 2]
assert feature_matrix_2[dfeat.get_name()].tolist() == [7, 10]
assert feature_matrix_2[agg_feat.get_name()].tolist() == [5, 1]
def test_cfm_no_cutoff_time_index(entityset):
es = entityset
agg_feat = Count(es['log']['id'], es['sessions'])
agg_feat4 = Sum(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat4, es['sessions'])
cutoff_time = pd.DataFrame({
'time': [datetime(2013, 4, 9, 10, 31, 19), datetime(2013, 4, 9, 11, 0, 0)],
'instance_id': [0, 2]
})
feature_matrix = calculate_feature_matrix([dfeat, agg_feat],
entityset,
cutoff_time_in_index=False,
approximate=Timedelta(12, 's'),
cutoff_time=cutoff_time)
assert feature_matrix.index.name == 'id'
assert feature_matrix.index.values.tolist() == [0, 2]
assert feature_matrix[dfeat.get_name()].tolist() == [10, 10]
assert feature_matrix[agg_feat.get_name()].tolist() == [5, 1]
cutoff_time = pd.DataFrame({
'time': [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)],
'instance_id': [0, 2]
})
feature_matrix_2 = calculate_feature_matrix([dfeat, agg_feat],
entityset,
cutoff_time_in_index=False,
approximate=Timedelta(10, 's'),
cutoff_time=cutoff_time)
assert feature_matrix_2.index.name == 'id'
assert feature_matrix_2.index.tolist() == [0, 2]
assert feature_matrix_2[dfeat.get_name()].tolist() == [7, 10]
assert feature_matrix_2[agg_feat.get_name()].tolist() == [5, 1]
def test_uses_full_entity_feat_of_approximate(entityset):
es = entityset
agg_feat = Sum(es['log']['value'], es['sessions'])
agg_feat2 = Sum(agg_feat, es['customers'])
agg_feat3 = Min(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat2, es['sessions'])
dfeat2 = DirectFeature(agg_feat3, es['sessions'])
p = Percentile(dfeat)
# only dfeat2 should be approximated
# because Percentile needs all values
feature_matrix_only_dfeat2 = calculate_feature_matrix(
[dfeat2],
instance_ids=[0, 2],
approximate=Timedelta(10, 's'),
cutoff_time_in_index=True,
cutoff_time=[
datetime(2011, 4, 9, 10, 31, 19),
datetime(2011, 4, 9, 11, 0, 0)
])
assert feature_matrix_only_dfeat2[dfeat2.get_name()].tolist() == [1, 0]
feature_matrix_approx = calculate_feature_matrix(
[p, dfeat, dfeat2, agg_feat],
instance_ids=[0, 2],
approximate=Timedelta(10, 's'),
cutoff_time_in_index=True,
cutoff_time=[
datetime(2011, 4, 9, 10, 31, 19),
datetime(2011, 4, 9, 11, 0, 0)
])
assert feature_matrix_only_dfeat2[dfeat2.get_name()].tolist(
) == feature_matrix_approx[dfeat2.get_name()].tolist()
feature_matrix_small_approx = calculate_feature_matrix(
[p, dfeat, dfeat2, agg_feat],
instance_ids=[0, 2],
approximate=Timedelta(10, 'ms'),
cutoff_time_in_index=True,
cutoff_time=[
datetime(2011, 4, 9, 10, 31, 19),
datetime(2011, 4, 9, 11, 0, 0)
])
feature_matrix_no_approx = calculate_feature_matrix(
[p, dfeat, dfeat2, agg_feat],
instance_ids=[0, 2],
cutoff_time_in_index=True,
cutoff_time=[
datetime(2011, 4, 9, 10, 31, 19),
datetime(2011, 4, 9, 11, 0, 0)
])
for f in [p, dfeat, agg_feat]:
for fm1, fm2 in combinations([
feature_matrix_approx, feature_matrix_small_approx,
feature_matrix_no_approx
], 2):
assert fm1[f.get_name()].tolist() == fm2[f.get_name()].tolist()
def test_uses_full_entity_feat_of_approximate(entityset):
es = entityset
agg_feat = Sum(es['log']['value'], es['sessions'])
agg_feat2 = Sum(agg_feat, es['customers'])
agg_feat3 = Min(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat2, es['sessions'])
dfeat2 = DirectFeature(agg_feat3, es['sessions'])
p = Percentile(dfeat)
# only dfeat2 should be approximated
# because Percentile needs all values
feature_matrix_only_dfeat2 = calculate_feature_matrix(
[dfeat2],
instance_ids=[0, 2],
approximate=Timedelta(10, 's'),
cutoff_time_in_index=True,
cutoff_time=[datetime(2011, 4, 9, 10, 31, 19),
datetime(2011, 4, 9, 11, 0, 0)])
assert feature_matrix_only_dfeat2[dfeat2.get_name()].tolist() == [1, 0]
feature_matrix_approx = calculate_feature_matrix(
[p, dfeat, dfeat2, agg_feat],
instance_ids=[0, 2],
approximate=Timedelta(10, 's'),
cutoff_time_in_index=True,
cutoff_time=[datetime(2011, 4, 9, 10, 31, 19),
datetime(2011, 4, 9, 11, 0, 0)])
assert feature_matrix_only_dfeat2[dfeat2.get_name()].tolist() == feature_matrix_approx[dfeat2.get_name()].tolist()
feature_matrix_small_approx = calculate_feature_matrix(
[p, dfeat, dfeat2, agg_feat],
instance_ids=[0, 2],
approximate=Timedelta(10, 'ms'),
cutoff_time_in_index=True,
cutoff_time=[datetime(2011, 4, 9, 10, 31, 19),
datetime(2011, 4, 9, 11, 0, 0)])
feature_matrix_no_approx = calculate_feature_matrix(
[p, dfeat, dfeat2, agg_feat],
instance_ids=[0, 2],
cutoff_time_in_index=True,
cutoff_time=[datetime(2011, 4, 9, 10, 31, 19),
datetime(2011, 4, 9, 11, 0, 0)])
for f in [p, dfeat, agg_feat]:
for fm1, fm2 in combinations([feature_matrix_approx,
feature_matrix_small_approx,
feature_matrix_no_approx], 2):
assert fm1[f.get_name()].tolist() == fm2[f.get_name()].tolist()
def test_training_window(entityset):
property_feature = Count(entityset['log']['id'], entityset['customers'])
top_level_agg = Count(entityset['customers']['id'], entityset[u'régions'])
# make sure features that have a direct to a higher level agg
# so we have multiple "filter eids" in get_pandas_data_slice,
# and we go through the loop to pull data with a training_window param more than once
dagg = DirectFeature(top_level_agg, entityset['customers'])
# for now, warns if last_time_index not present
times = [datetime(2011, 4, 9, 12, 31),
datetime(2011, 4, 10, 11),
datetime(2011, 4, 10, 13, 10, 1)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 1, 2]})
feature_matrix = calculate_feature_matrix([property_feature, dagg],
entityset,
cutoff_time=cutoff_time,
training_window='2 hours')
entityset.add_last_time_indexes()
with pytest.raises(AssertionError):
feature_matrix = calculate_feature_matrix([property_feature],
entityset,
cutoff_time=cutoff_time,
training_window=Timedelta(2, 'observations', entity='log'))
feature_matrix = calculate_feature_matrix([property_feature, dagg],
entityset,
cutoff_time=cutoff_time,
training_window='2 hours')
prop_values = [5, 5, 1]
dagg_values = [3, 2, 1]
assert (feature_matrix[property_feature.get_name()] == prop_values).values.all()
assert (feature_matrix[dagg.get_name()] == dagg_values).values.all()
def test_training_window(entityset):
property_feature = Count(entityset['log']['id'], entityset['customers'])
top_level_agg = Count(entityset['customers']['id'], entityset[u'régions'])
# make sure features that have a direct to a higher level agg
# so we have multiple "filter eids" in get_pandas_data_slice,
# and we go through the loop to pull data with a training_window param more than once
dagg = DirectFeature(top_level_agg, entityset['customers'])
# for now, warns if last_time_index not present
times = [datetime(2011, 4, 9, 12, 31),
datetime(2011, 4, 10, 11),
datetime(2011, 4, 10, 13, 10, 1)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 1, 2]})
feature_matrix = calculate_feature_matrix([property_feature, dagg],
entityset,
cutoff_time=cutoff_time,
training_window='2 hours')
entityset.add_last_time_indexes()
with pytest.raises(AssertionError):
feature_matrix = calculate_feature_matrix([property_feature],
entityset,
cutoff_time=cutoff_time,
training_window=Timedelta(2, 'observations', entity='log'))
feature_matrix = calculate_feature_matrix([property_feature, dagg],
entityset,
cutoff_time=cutoff_time,
training_window='2 hours')
prop_values = [5, 5, 1]
dagg_values = [3, 2, 1]
assert (feature_matrix[property_feature.get_name()] == prop_values).values.all()
assert (feature_matrix[dagg.get_name()] == dagg_values).values.all()
def test_make_dfeat_of_agg_feat_through_parent(entityset, backend):
"""
The graph looks like this:
R C = Customers, the entity we're trying to predict on
/ \ R = Regions, a parent of customers
S C S = Stores, a child of regions
|
etc.
We're trying to calculate a DFeat from C to R on an agg_feat of R on S.
"""
store_id_feat = IdentityFeature(entityset['stores']['id'])
store_count_feat = Count(store_id_feat,
parent_entity=entityset['regions'])
num_stores_feat = DirectFeature(store_count_feat,
child_entity=entityset['customers'])
pandas_backend = backend([num_stores_feat])
df = pandas_backend.calculate_all_features(instance_ids=[0],
time_last=None)
v = df[num_stores_feat.get_name()][0]
assert (v == 3)
def test_make_dfeat_of_agg_feat_through_parent(entityset, backend):
"""
The graph looks like this:
R C = Customers, the entity we're trying to predict on
/ \ R = Regions, a parent of customers
S C S = Stores, a child of regions
|
etc.
We're trying to calculate a DFeat from C to R on an agg_feat of R on S.
"""
store_id_feat = IdentityFeature(entityset['stores']['id'])
store_count_feat = Count(store_id_feat,
parent_entity=entityset[u'régions'])
num_stores_feat = DirectFeature(store_count_feat,
child_entity=entityset['customers'])
pandas_backend = backend([num_stores_feat])
df = pandas_backend.calculate_all_features(instance_ids=[0],
time_last=None)
v = df[num_stores_feat.get_name()][0]
assert (v == 3)
def test_approximate_dfeat_of_need_all_values(entityset):
es = entityset
p = Percentile(es['log']['value'])
agg_feat = Sum(p, es['sessions'])
agg_feat2 = Sum(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat2, es['sessions'])
times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]})
feature_matrix = calculate_feature_matrix([dfeat, agg_feat],
entityset,
approximate=Timedelta(10, 's'),
cutoff_time_in_index=True,
cutoff_time=cutoff_time)
log_df = es['log'].df
instances = [0, 2]
cutoffs = [pd.Timestamp('2011-04-09 10:31:19'), pd.Timestamp('2011-04-09 11:00:00')]
approxes = [pd.Timestamp('2011-04-09 10:31:10'), pd.Timestamp('2011-04-09 11:00:00')]
true_vals = []
true_vals_approx = []
for instance, cutoff, approx in zip(instances, cutoffs, approxes):
log_data_cutoff = log_df[log_df['datetime'] < cutoff]
log_data_cutoff['percentile'] = log_data_cutoff['value'].rank(pct=True)
true_agg = log_data_cutoff.loc[log_data_cutoff['session_id'] == instance, 'percentile'].fillna(0).sum()
true_vals.append(round(true_agg, 3))
log_data_approx = log_df[log_df['datetime'] < approx]
log_data_approx['percentile'] = log_data_approx['value'].rank(pct=True)
true_agg_approx = log_data_approx.loc[log_data_approx['session_id'].isin([0, 1, 2]), 'percentile'].fillna(0).sum()
true_vals_approx.append(round(true_agg_approx, 3))
lapprox = [round(x, 3) for x in feature_matrix[dfeat.get_name()].tolist()]
test_list = [round(x, 3) for x in feature_matrix[agg_feat.get_name()].tolist()]
assert lapprox == true_vals_approx
assert test_list == true_vals
def test_approximate_dfeat_of_need_all_values(entityset):
es = entityset
p = Percentile(es['log']['value'])
agg_feat = Sum(p, es['sessions'])
agg_feat2 = Sum(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat2, es['sessions'])
feature_matrix = calculate_feature_matrix([dfeat, agg_feat],
entityset,
instance_ids=[0, 2],
approximate=Timedelta(10, 's'),
cutoff_time_in_index=True,
cutoff_time=[datetime(2011, 4, 9, 10, 31, 19),
datetime(2011, 4, 9, 11, 0, 0)])
log_df = es['log'].df
instances = [0, 2]
cutoffs = [pd.Timestamp('2011-04-09 10:31:19'), pd.Timestamp('2011-04-09 11:00:00')]
approxes = [pd.Timestamp('2011-04-09 10:31:10'), pd.Timestamp('2011-04-09 11:00:00')]
true_vals = []
true_vals_approx = []
for instance, cutoff, approx in zip(instances, cutoffs, approxes):
log_data_cutoff = log_df[log_df['datetime'] < cutoff]
log_data_cutoff['percentile'] = log_data_cutoff['value'].rank(pct=True)
true_agg = log_data_cutoff.loc[log_data_cutoff['session_id'] == instance, 'percentile'].fillna(0).sum()
true_vals.append(round(true_agg, 3))
log_data_approx = log_df[log_df['datetime'] < approx]
log_data_approx['percentile'] = log_data_approx['value'].rank(pct=True)
true_agg_approx = log_data_approx.loc[log_data_approx['session_id'].isin([0, 1, 2]), 'percentile'].fillna(0).sum()
true_vals_approx.append(round(true_agg_approx, 3))
lapprox = [round(x, 3) for x in feature_matrix[dfeat.get_name()].tolist()]
test_list = [round(x, 3) for x in feature_matrix[agg_feat.get_name()].tolist()]
assert lapprox == true_vals_approx
assert test_list == true_vals
def test_handles_training_window_correctly(entityset):
property_feature = Count(entityset['log']['id'], entityset['customers'])
top_level_agg = Count(entityset['customers']['id'], entityset['regions'])
# make sure we test feature target entity training window
feature_matrix = calculate_feature_matrix(
[property_feature],
instance_ids=[0, 1, 2],
cutoff_time=[
datetime(2011, 4, 9, 12, 31),
datetime(2011, 4, 10, 11),
datetime(2011, 4, 10, 13, 10, 1)
],
training_window={'customers': '36 hours'})
prop_values = [0, 5, 0]
assert (feature_matrix[property_feature.get_name()] == prop_values
).values.all()
# make sure features that have a direct to a higher level agg
# so we have multiple "filter eids" in get_pandas_data_slice,
# and we go through the loop to pull data with a training_window param more than once
dagg = DirectFeature(top_level_agg, entityset['customers'])
feature_matrix = calculate_feature_matrix(
[property_feature, dagg],
instance_ids=[0, 1, 2],
cutoff_time=[
datetime(2011, 4, 9, 12, 31),
datetime(2011, 4, 10, 11),
datetime(2011, 4, 10, 13, 10, 1)
],
training_window={'log': '2 hours'})
prop_values = [5, 5, 1]
dagg_values = [3, 3, 3]
assert (feature_matrix[property_feature.get_name()] == prop_values
).values.all()
assert (feature_matrix[dagg.get_name()] == dagg_values).values.all()
property_feature = Count(entityset['log']['id'], entityset['customers'])
feature_matrix = calculate_feature_matrix([property_feature],
instance_ids=[0, 1, 2],
cutoff_time=[
datetime(2011, 4, 9, 12, 31),
datetime(2011, 4, 10, 11),
datetime(
2011, 4, 10, 13, 10, 1)
],
training_window='2 hours')
labels = [0, 0, 0]
assert (feature_matrix == labels).values.all()
with pytest.raises(AssertionError):
feature_matrix = calculate_feature_matrix(
[property_feature],
instance_ids=[0, 1, 2],
cutoff_time=[
datetime(2011, 4, 9, 12, 31),
datetime(2011, 4, 10, 11),
datetime(2011, 4, 10, 13, 10, 1)
],
training_window=Timedelta(2, 'observations', entity='log'))
def test_make_dfeat(entityset, backend):
f = DirectFeature(entityset['customers']['age'],
child_entity=entityset['sessions'])
pandas_backend = backend([f])
df = pandas_backend.calculate_all_features(instance_ids=[0],
time_last=None)
v = df[f.get_name()][0]
assert (v == 33)
def test_make_dfeat(entityset, backend):
f = DirectFeature(entityset['customers']['age'],
child_entity=entityset['sessions'])
pandas_backend = backend([f])
df = pandas_backend.calculate_all_features(instance_ids=[0],
time_last=None)
v = df[f.get_name()][0]
assert (v == 33)
def test_direct_from_variable(es):
# should be same behavior as test_direct_from_identity
d = DirectFeature(base_feature=es['sessions']['device_type'],
child_entity=es['log'])
pandas_backend = PandasBackend(es, [d])
df = pandas_backend.calculate_all_features(instance_ids=[0, 5],
time_last=None)
v = df[d.get_name()].tolist()
assert v == [0, 1]
def test_direct_from_variable(es):
# should be same behavior as test_direct_from_identity
d = DirectFeature(base_feature=es['sessions']['device_type'],
child_entity=es['log'])
pandas_backend = PandasBackend(es, [d])
df = pandas_backend.calculate_all_features(instance_ids=[0, 5],
time_last=None)
v = df[d.get_name()].tolist()
assert v == [0, 1]
def test_direct_rename(es):
# should be same behavior as test_direct_from_identity
feat = DirectFeature(base_feature=es['sessions']['device_type'],
child_entity=es['log'])
copy_feat = feat.rename("session_test")
assert feat.hash() != copy_feat.hash()
assert feat.get_name() != copy_feat.get_name()
assert feat.base_features[0].generate_name() == copy_feat.base_features[0].generate_name()
assert feat.entity == copy_feat.entity
def test_direct_rename(es):
# should be same behavior as test_direct_from_identity
feat = DirectFeature(base_feature=es['sessions']['device_type'],
child_entity=es['log'])
copy_feat = feat.rename("session_test")
assert feat.hash() != copy_feat.hash()
assert feat.get_name() != copy_feat.get_name()
assert feat.base_features[0]._get_name(
) == copy_feat.base_features[0]._get_name()
assert feat.entity == copy_feat.entity
def test_approximate_dfeat_of_dfeat_of_agg_on_target(entityset):
es = entityset
agg_feat = Count(es['log']['id'], es['sessions'])
agg_feat2 = Sum(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat2, es['log'])
times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]})
feature_matrix = calculate_feature_matrix([dfeat],
entityset,
approximate=Timedelta(10, 's'),
cutoff_time=cutoff_time)
assert feature_matrix[dfeat.get_name()].tolist() == [7, 10]
def test_approximate_dfeat_of_dfeat_of_agg_on_target(entityset):
es = entityset
agg_feat = Count(es['log']['id'], es['sessions'])
agg_feat2 = Sum(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat2, es['log'])
times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]})
feature_matrix = calculate_feature_matrix([dfeat],
entityset,
approximate=Timedelta(10, 's'),
cutoff_time=cutoff_time)
assert feature_matrix[dfeat.get_name()].tolist() == [7, 10]
def test_approximate_returns_correct_empty_default_values(entityset):
es = entityset
agg_feat = Count(es['log']['id'], es['customers'])
dfeat = DirectFeature(agg_feat, es['sessions'])
cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-08 11:00:00'),
pd.Timestamp('2011-04-09 11:00:00')],
'instance_id': [0, 0]})
fm = calculate_feature_matrix([dfeat],
approximate=Timedelta(10, 's'),
cutoff_time=cutoff_df)
assert fm[dfeat.get_name()].tolist() == [0, 10]
def test_approximate_multiple_instances_per_cutoff_time(entityset):
es = entityset
agg_feat = Count(es['log']['id'], es['sessions'])
agg_feat2 = Sum(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat2, es['sessions'])
times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]})
feature_matrix = calculate_feature_matrix([dfeat, agg_feat],
entityset,
approximate=Timedelta(1, 'week'),
cutoff_time=cutoff_time,
chunk_size="cutoff time")
assert feature_matrix.shape[0] == 2
assert feature_matrix[dfeat.get_name()].dropna().shape[0] == 0
assert feature_matrix[agg_feat.get_name()].tolist() == [5, 1]
def test_approximate_multiple_instances_per_cutoff_time(entityset):
es = entityset
agg_feat = Count(es['log']['id'], es['sessions'])
agg_feat2 = Sum(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat2, es['sessions'])
times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]})
feature_matrix = calculate_feature_matrix([dfeat, agg_feat],
entityset,
approximate=Timedelta(1, 'week'),
cutoff_time=cutoff_time,
chunk_size="cutoff time")
assert feature_matrix.shape[0] == 2
assert feature_matrix[dfeat.get_name()].dropna().shape[0] == 0
assert feature_matrix[agg_feat.get_name()].tolist() == [5, 1]
def test_empty_path_approximate_partial(entityset):
es = copy.deepcopy(entityset)
es['sessions'].df['customer_id'] = [0, 0, np.nan, 1, 1, 2]
agg_feat = Count(es['log']['id'], es['sessions'])
agg_feat2 = Sum(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat2, es['sessions'])
times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]})
feature_matrix = calculate_feature_matrix([dfeat, agg_feat],
es,
approximate=Timedelta(10, 's'),
cutoff_time=cutoff_time)
vals1 = feature_matrix[dfeat.get_name()].tolist()
assert vals1[0] == 7
assert np.isnan(vals1[1])
assert feature_matrix[agg_feat.get_name()].tolist() == [5, 1]
def test_empty_path_approximate_partial(entityset):
es = copy.deepcopy(entityset)
es['sessions'].df['customer_id'] = [0, 0, np.nan, 1, 1, 2]
agg_feat = Count(es['log']['id'], es['sessions'])
agg_feat2 = Sum(agg_feat, es['customers'])
dfeat = DirectFeature(agg_feat2, es['sessions'])
times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]})
feature_matrix = calculate_feature_matrix([dfeat, agg_feat],
es,
approximate=Timedelta(10, 's'),
cutoff_time=cutoff_time)
vals1 = feature_matrix[dfeat.get_name()].tolist()
assert vals1[0] == 7
assert np.isnan(vals1[1])
assert feature_matrix[agg_feat.get_name()].tolist() == [5, 1]
def test_approximate_child_aggs_handled_correctly(entityset):
es = entityset
agg_feat = Count(es['customers']['id'], es['regions'])
dfeat = DirectFeature(agg_feat, es['customers'])
agg_feat_2 = Count(es['log']['value'], es['customers'])
cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-08 10:30:00'),
pd.Timestamp('2011-04-09 10:30:06')],
'instance_id': [0, 0]})
fm = calculate_feature_matrix([dfeat],
approximate=Timedelta(10, 's'),
cutoff_time=cutoff_df)
fm_2 = calculate_feature_matrix([dfeat, agg_feat_2],
approximate=Timedelta(10, 's'),
cutoff_time=cutoff_df)
assert fm[dfeat.get_name()].tolist() == [2, 3]
assert fm_2[agg_feat_2.get_name()].tolist() == [0, 2]
def test_approx_base_feature_is_also_first_class_feature(entityset):
es = entityset
log_to_products = DirectFeature(es['products']['rating'], es['log'])
# This should still be computed properly
agg_feat = Min(log_to_products, es['sessions'])
customer_agg_feat = Sum(agg_feat, es['customers'])
# This is to be approximated
sess_to_cust = DirectFeature(customer_agg_feat, es['sessions'])
times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]})
feature_matrix = calculate_feature_matrix([sess_to_cust, agg_feat],
entityset,
approximate=Timedelta(10, 's'),
cutoff_time=cutoff_time)
vals1 = feature_matrix[sess_to_cust.get_name()].tolist()
assert vals1 == [8.5, 7]
vals2 = feature_matrix[agg_feat.get_name()].tolist()
assert vals2 == [4, 1.5]
def test_approx_base_feature_is_also_first_class_feature(entityset):
es = entityset
log_to_products = DirectFeature(es['products']['rating'], es['log'])
# This should still be computed properly
agg_feat = Min(log_to_products, es['sessions'])
customer_agg_feat = Sum(agg_feat, es['customers'])
# This is to be approximated
sess_to_cust = DirectFeature(customer_agg_feat, es['sessions'])
times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]})
feature_matrix = calculate_feature_matrix([sess_to_cust, agg_feat],
entityset,
approximate=Timedelta(10, 's'),
cutoff_time=cutoff_time)
vals1 = feature_matrix[sess_to_cust.get_name()].tolist()
assert vals1 == [8.5, 7]
vals2 = feature_matrix[agg_feat.get_name()].tolist()
assert vals2 == [4, 1.5]
def test_training_window_recent_time_index(entityset):
# customer with no sessions
row = {
'id': [3],
'age': [73],
u'région_id': ['United States'],
'cohort': [1],
'cohort_name': ["Late Adopters"],
'loves_ice_cream': [True],
'favorite_quote': ["Who is John Galt?"],
'signup_date': [datetime(2011, 4, 10)],
'upgrade_date': [datetime(2011, 4, 12)],
'cancel_date': [datetime(2011, 5, 13)],
'date_of_birth': [datetime(1938, 2, 1)],
'engagement_level': [2],
}
df = pd.DataFrame(row)
df.index = range(3, 4)
df = entityset['customers'].df.append(df, sort=False)
entityset['customers'].update_data(df=df,
recalculate_last_time_indexes=False)
entityset.add_last_time_indexes()
property_feature = Count(entityset['log']['id'], entityset['customers'])
top_level_agg = Count(entityset['customers']['id'], entityset[u'régions'])
dagg = DirectFeature(top_level_agg, entityset['customers'])
instance_ids = [0, 1, 2, 3]
times = [
datetime(2011, 4, 9, 12, 31),
datetime(2011, 4, 10, 11),
datetime(2011, 4, 10, 13, 10, 1),
datetime(2011, 4, 10, 1, 59, 59)
]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': instance_ids})
feature_matrix = calculate_feature_matrix([property_feature, dagg],
entityset,
cutoff_time=cutoff_time,
training_window='2 hours')
prop_values = [5, 5, 1, 0]
dagg_values = [3, 2, 1, 3]
feature_matrix.sort_index(inplace=True)
assert (feature_matrix[property_feature.get_name()] == prop_values
).values.all()
assert (feature_matrix[dagg.get_name()] == dagg_values).values.all()
def test_dfs_builds_on_seed_features_more_than_max_depth(es):
seed_feature_sessions = Count(es['log']["id"], es['sessions']) > 2
seed_feature_log = Hour(es['log']['datetime'])
session_agg = Last(seed_feature_log, es['sessions'])
# Depth of this feat is 2 relative to session_agg, the seed feature,
# which is greater than max_depth so it shouldn't be built
session_agg_trans = DirectFeature(Count(session_agg, es['customers']),
es['sessions'])
dfs_obj = DeepFeatureSynthesis(
target_entity_id='sessions',
entityset=es,
agg_primitives=[Last, Count],
trans_primitives=[],
max_depth=1,
seed_features=[seed_feature_sessions, seed_feature_log])
features = dfs_obj.build_features()
assert seed_feature_sessions.get_name() in [f.get_name() for f in features]
assert session_agg.get_name() in [f.get_name() for f in features]
assert session_agg_trans.get_name() not in [f.get_name() for f in features]
def test_training_window_recent_time_index(entityset):
# customer with no sessions
row = {
'id': [3],
'age': [73],
u'région_id': ['United States'],
'cohort': [1],
'cancel_reason': ["I am finally awake!!"],
'loves_ice_cream': [True],
'favorite_quote': ["Who is John Galt?"],
'signup_date': [datetime(2011, 4, 10)],
'upgrade_date': [datetime(2011, 4, 12)],
'cancel_date': [datetime(2011, 5, 13)],
'date_of_birth': [datetime(1938, 2, 1)],
'engagement_level': [2],
}
df = pd.DataFrame(row)
df.index = range(3, 4)
df = entityset['customers'].df.append(df, sort=False)
entityset['customers'].update_data(df=df,
recalculate_last_time_indexes=False)
entityset.add_last_time_indexes()
property_feature = Count(entityset['log']['id'], entityset['customers'])
top_level_agg = Count(entityset['customers']['id'], entityset[u'régions'])
dagg = DirectFeature(top_level_agg, entityset['customers'])
instance_ids = [0, 1, 2, 3]
times = [datetime(2011, 4, 9, 12, 31), datetime(2011, 4, 10, 11),
datetime(2011, 4, 10, 13, 10, 1), datetime(2011, 4, 10, 1, 59, 59)]
cutoff_time = pd.DataFrame({'time': times, 'instance_id': instance_ids})
feature_matrix = calculate_feature_matrix(
[property_feature, dagg],
entityset,
cutoff_time=cutoff_time,
training_window='2 hours'
)
prop_values = [5, 5, 1, 0]
dagg_values = [3, 2, 1, 3]
feature_matrix.sort_index(inplace=True)
assert (feature_matrix[property_feature.get_name()] == prop_values).values.all()
assert (feature_matrix[dagg.get_name()] == dagg_values).values.all()
def test_dfs_builds_on_seed_features_more_than_max_depth(es):
seed_feature_sessions = Count(es['log']["id"], es['sessions']) > 2
seed_feature_log = Hour(es['log']['datetime'])
session_agg = Last(seed_feature_log, es['sessions'])
# Depth of this feat is 2 relative to session_agg, the seed feature,
# which is greater than max_depth so it shouldn't be built
session_agg_trans = DirectFeature(Count(session_agg, es['customers']),
es['sessions'])
dfs_obj = DeepFeatureSynthesis(target_entity_id='sessions',
entityset=es,
agg_primitives=[Last, Count],
trans_primitives=[],
max_depth=1,
seed_features=[seed_feature_sessions,
seed_feature_log])
features = dfs_obj.build_features()
assert seed_feature_sessions.get_name() in [f.get_name()
for f in features]
assert session_agg.get_name() in [f.get_name() for f in features]
assert session_agg_trans.get_name() not in [f.get_name()
for f in features]
def test_make_dfeat_of_agg_feat_on_self(entityset, backend):
"""
The graph looks like this:
R R = Regions, a parent of customers
|
C C = Customers, the entity we're trying to predict on
|
etc.
We're trying to calculate a DFeat from C to R on an agg_feat of R on C.
"""
customer_count_feat = Count(entityset['customers']['id'],
parent_entity=entityset['regions'])
num_customers_feat = DirectFeature(customer_count_feat,
child_entity=entityset['customers'])
pandas_backend = backend([num_customers_feat])
df = pandas_backend.calculate_all_features(instance_ids=[0],
time_last=None)
v = df[num_customers_feat.get_name()][0]
assert (v == 3)
def test_make_dfeat_of_agg_feat_on_self(entityset, backend):
"""
The graph looks like this:
R R = Regions, a parent of customers
|
C C = Customers, the entity we're trying to predict on
|
etc.
We're trying to calculate a DFeat from C to R on an agg_feat of R on C.
"""
customer_count_feat = Count(entityset['customers']['id'],
parent_entity=entityset[u'régions'])
num_customers_feat = DirectFeature(customer_count_feat,
child_entity=entityset['customers'])
pandas_backend = backend([num_customers_feat])
df = pandas_backend.calculate_all_features(instance_ids=[0],
time_last=None)
v = df[num_customers_feat.get_name()][0]
assert (v == 3)
